---
status: complete
phase: 03-training-energy-tests
source: [03-01-SUMMARY.md, 03-02-SUMMARY.md]
started: 2026-02-24T21:10:00Z
updated: 2026-02-24T21:22:00Z
---

## Current Test

[testing complete]

## Tests

### 1. Public API imports
expected: Running `from pcn_torch import train_pcn, test_pcn, TrainConfig, TrainHistory, EnergyHistory, TrainCallback, RichCallback, compute_energy, compute_energy_per_layer` succeeds with no errors.
result: pass

### 2. Train a PCN on classification data
expected: Running train_pcn with a small PCN and random data completes without errors and returns a TrainHistory object with populated train_accuracy list (length = num_epochs).
result: pass

### 3. Evaluate with test_pcn
expected: Running test_pcn on a trained model returns a dict containing 'accuracy' (float) and 'energy' (float) keys.
result: pass

### 4. Energy tracking populated after training
expected: After training, history.energy.per_batch is a non-empty list and history.energy.per_epoch is a non-empty list with length equal to num_epochs.
result: pass

### 5. TrainConfig paper defaults
expected: TrainConfig() with no arguments has T_infer=50, lr_infer=0.05, lr_learn=0.005, num_epochs=4, task="classification".
result: pass

### 6. Full test suite passes
expected: Running `pytest tests/` passes all 101 tests with no failures or errors.
result: pass

### 7. Regression task support
expected: Running train_pcn with TrainConfig(task="regression") and continuous targets trains without error; history has no train_accuracy (or it's empty).
result: pass

## Summary

total: 7
passed: 7
issues: 0
pending: 0
skipped: 0

## Gaps

[none yet]
